



import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
A__								=		logging.get_logger(__name__)


A__								=		'''https://openaipublic.azureedge.net/jukebox/models/'''
A__								=		{
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}



def      _lowerCAmelCase			(   __lowerCAmelCase )  ->   Optional[int]:


				"""simple docstring"""

				if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
								snake_case__      : Union[str, Any]							=      key.replace('''.model.1.bias'''    ,    '''.conv1d_1.bias''' )
				elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
								snake_case__      : Union[str, Any]							=      key.replace('''.model.1.weight'''    ,    '''.conv1d_1.weight''' )
				elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
								snake_case__      : str							=      key.replace('''.model.3.bias'''    ,    '''.conv1d_2.bias''' )
				elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
								snake_case__      : Tuple							=      key.replace('''.model.3.weight'''    ,    '''.conv1d_2.weight''' )

				if "conditioner_blocks.0." in key:
								snake_case__      : List[Any]							=      key.replace('''conditioner_blocks.0'''    ,    '''conditioner_blocks''' )

				if "prime_prior" in key:
								snake_case__      : List[str]							=      key.replace('''prime_prior'''    ,    '''encoder''' )

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								snake_case__      : Union[str, Any]							=      key.replace('''.emb.'''    ,    '''.''' )

				if key.endswith('''k''' ):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace('''.k'''    ,    '''.codebook''' )
				if "y_emb." in key:
								return key.replace('''y_emb.'''    ,    '''metadata_embedding.''' )

				if "x_emb.emb." in key:
								snake_case__      : Any							=      key.replace('''0.x_emb.emb'''    ,    '''embed_tokens''' )

				if "prime_state_ln" in key:
								return key.replace('''prime_state_ln'''    ,    '''encoder.final_layer_norm''' )
				if ".ln" in key:
								return key.replace('''.ln'''    ,    '''.layer_norm''' )
				if "_ln" in key:
								return key.replace('''_ln'''    ,    '''_layer_norm''' )

				if "prime_state_proj" in key:
								return key.replace('''prime_state_proj'''    ,    '''encoder.proj_in''' )
				if "prime_x_out" in key:
								return key.replace('''prime_x_out'''    ,    '''encoder.lm_head''' )
				if "prior.x_out" in key:
								return key.replace('''x_out'''    ,    '''fc_proj_out''' )
				if "x_emb" in key:
								return key.replace('''x_emb'''    ,    '''embed_tokens''' )

				return key



def      _lowerCAmelCase			(   __lowerCAmelCase    ,    __lowerCAmelCase    ,    __lowerCAmelCase    ,    __lowerCAmelCase )  ->   Optional[Any]:


				"""simple docstring"""

				snake_case__      : List[Any]							=      {}
				import re

				snake_case__      : Union[str, Any]							=      re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
				snake_case__      : Optional[Any]							=      re.compile(
				    r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
				snake_case__      : Optional[Any]							=      re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

				snake_case__      : Dict							=      re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
				snake_case__      : Tuple							=      re.compile(
				    r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
				snake_case__      : List[str]							=      re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

				snake_case__      : Dict							=      re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
				snake_case__      : Optional[Any]							=      re.compile(
				    r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
				snake_case__      : Dict							=      re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''' )

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(UpperCamelCase__ ):
												snake_case__      : int							=      re_encoder_block_conv_in.match(UpperCamelCase__ )
												snake_case__      : Optional[Any]							=      regex_match.groups()
												snake_case__      : Tuple							=      int(groups[2] ) * 2 + int(groups[3] )
												snake_case__      : Optional[Any]							=      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
												snake_case__      : Any							=      re_encoder_block_conv_in.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_encoder_block_resnet.fullmatch(UpperCamelCase__ ):
												snake_case__      : List[Any]							=      re_encoder_block_resnet.match(UpperCamelCase__ )
												snake_case__      : List[str]							=      regex_match.groups()
												snake_case__      : Tuple							=      int(groups[2] ) * 2 + int(groups[3] )
												snake_case__      : Any							=      {'''1''': 1, '''3''': 2}[groups[-2]]
												snake_case__      : Any							=      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
												snake_case__      : Optional[int]							=      f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__      : Union[str, Any]							=      prefix + resnet_block
												snake_case__      : List[Any]							=      re_encoder_block_resnet.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__ ):
												snake_case__      : Any							=      re_encoder_block_proj_out.match(UpperCamelCase__ )
												snake_case__      : List[Any]							=      regex_match.groups()
												snake_case__      : Dict							=      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
												snake_case__      : Optional[Any]							=      re_encoder_block_proj_out.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(UpperCamelCase__ ):
												snake_case__      : Union[str, Any]							=      re_decoder_block_conv_out.match(UpperCamelCase__ )
												snake_case__      : Optional[Any]							=      regex_match.groups()
												snake_case__      : Tuple							=      int(groups[2] ) * 2 + int(groups[3] ) - 2
												snake_case__      : Optional[Any]							=      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
												snake_case__      : Optional[Any]							=      re_decoder_block_conv_out.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_decoder_block_resnet.fullmatch(UpperCamelCase__ ):
												snake_case__      : Union[str, Any]							=      re_decoder_block_resnet.match(UpperCamelCase__ )
												snake_case__      : Any							=      regex_match.groups()
												snake_case__      : int							=      int(groups[2] ) * 2 + int(groups[3] ) - 2
												snake_case__      : Tuple							=      {'''1''': 1, '''3''': 2}[groups[-2]]
												snake_case__      : Optional[int]							=      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
												snake_case__      : int							=      f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__      : int							=      prefix + resnet_block
												snake_case__      : Dict							=      re_decoder_block_resnet.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__ ):
												snake_case__      : Any							=      re_decoder_block_proj_in.match(UpperCamelCase__ )
												snake_case__      : int							=      regex_match.groups()
												snake_case__      : int							=      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
												snake_case__      : Optional[Any]							=      re_decoder_block_proj_in.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(UpperCamelCase__ ):
												snake_case__      : Union[str, Any]							=      re_prior_cond_conv_out.match(UpperCamelCase__ )
												snake_case__      : Tuple							=      regex_match.groups()
												snake_case__      : Tuple							=      int(groups[1] ) * 2 + int(groups[2] ) - 2
												snake_case__      : Tuple							=      f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
												snake_case__      : List[str]							=      re_prior_cond_conv_out.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_prior_cond_resnet.fullmatch(UpperCamelCase__ ):
												snake_case__      : Optional[Any]							=      re_prior_cond_resnet.match(UpperCamelCase__ )
												snake_case__      : str							=      regex_match.groups()
												snake_case__      : Union[str, Any]							=      int(groups[1] ) * 2 + int(groups[2] ) - 2
												snake_case__      : int							=      {'''1''': 1, '''3''': 2}[groups[-2]]
												snake_case__      : Optional[Any]							=      f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
												snake_case__      : Dict							=      f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__      : Tuple							=      prefix + resnet_block
												snake_case__      : int							=      re_prior_cond_resnet.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__ ):
												snake_case__      : List[Any]							=      re_prior_cond_proj_in.match(UpperCamelCase__ )
												snake_case__      : str							=      regex_match.groups()
												snake_case__      : int							=      f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
												snake_case__      : Any							=      re_prior_cond_proj_in.sub(UpperCamelCase__    ,    UpperCamelCase__ )

								# keep original key
								else:
												snake_case__      : Tuple							=      original_key

								snake_case__      : Optional[Any]							=      replace_key(UpperCamelCase__ )

								if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
												print(f"""failed converting {original_key} to {key}, does not match""" )

								# handle missmatched shape
								elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
												snake_case__      : Optional[int]							=      model_state_dict[f"""{key_prefix}.{key}"""]
												print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""" )
												snake_case__      : Union[str, Any]							=      original_key

								snake_case__      : Any							=      original_key
								snake_case__      : List[str]							=      value

				return new_dict



@torch.no_grad()
def      _lowerCAmelCase			(   __lowerCAmelCase=None    ,    __lowerCAmelCase=None )  ->   Any:


				"""simple docstring"""

				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/' )[-1]}""" ):
												snake_case__      : int							=      requests.get(f"""{PREFIX}{file}"""    ,    allow_redirects=UpperCamelCase__ )
												os.makedirs(f"""{pytorch_dump_folder_path}/"""    ,    exist_ok=UpperCamelCase__ )
												open(f"""{pytorch_dump_folder_path}/{file.split('/' )[-1]}"""    ,    '''wb''' ).write(r.content )

				snake_case__      : Any							=      MODEL_MAPPING[model_name.split('''/''' )[-1]]

				snake_case__      : int							=      JukeboxConfig.from_pretrained(UpperCamelCase__ )
				snake_case__      : List[str]							=      JukeboxModel(UpperCamelCase__ )

				snake_case__      : Any							=      []
				snake_case__      : Dict							=      {}
				for i, dict_name in enumerate(UpperCamelCase__ ):
								snake_case__      : Tuple							=      torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}""" )['''model''']

								snake_case__      : List[str]							=      {}
								for k in old_dic.keys():
												if k.endswith('''.b''' ):
																snake_case__      : Optional[Any]							=      old_dic[k]
												elif k.endswith('''.w''' ):
																snake_case__      : int							=      old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																snake_case__      : Optional[Any]							=      old_dic[k]
												else:
																snake_case__      : str							=      old_dic[k]

								snake_case__      : Tuple							=      '''vqvae''' if i == 0 else f"""priors.{3 - i}"""
								snake_case__      : Tuple							=      fix_jukebox_keys(UpperCamelCase__    ,    model.state_dict()    ,    UpperCamelCase__    ,    UpperCamelCase__ )
								weight_dict.append(UpperCamelCase__ )

				snake_case__      : Dict							=      weight_dict.pop(0 )
				model.vqvae.load_state_dict(UpperCamelCase__ )
				for i in range(len(UpperCamelCase__ ) ):
								model.priors[i].load_state_dict(weight_dict[2 - i] )

				Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
				with open(f"""{pytorch_dump_folder_path}/mapping.json"""    ,    '''w''' ) as txtfile:
								json.dump(UpperCamelCase__    ,    UpperCamelCase__ )

				print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
				model.save_pretrained(UpperCamelCase__ )

				return weight_dict


if __name__ == "__main__":
				A__								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''jukebox-5b-lyrics''',
				    type=str,
				    help='''Name of the model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default='''jukebox-5b-lyrics-converted''',
				    type=str,
				    help='''Path to the output PyTorch model directory.''',
				)
				A__								=		parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    return base * power(UpperCamelCase__							,    (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
  print("Raise base to the power of exponent using recursion...")
  __UpperCAmelCase					=int(input("Enter the base: ").strip())
  __UpperCAmelCase					=int(input("Enter the exponent: ").strip())
  __UpperCAmelCase					=power(base, abs(exponent))
  if exponent < 0:  # power() does not properly deal w/ negative exponents
    __UpperCAmelCase					=1 / result
  print(f'{base} to the power of {exponent} is {result}')
